Update app.py

app.py

@@ -1,154 +1,776 @@
+# ===== 必须首先导入spaces =====
+try:
+    import spaces
+    SPACES_AVAILABLE = True
+    print("✅ Spaces available - ZeroGPU mode")
+except ImportError:
+    SPACES_AVAILABLE = False
+    print("⚠️ Spaces not available - running in regular mode")
+
+# ===== 其他导入 =====
+import os
+import uuid
+from datetime import datetime
+import random
+import torch
 import gradio as gr
+from diffusers import FluxPipeline, FlowMatchEulerDiscreteScheduler
+from PIL import Image
+import traceback
 import numpy as np
-import random
+import io
+import base64
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
+# ===== 长提示词处理 =====
+try:
+    from compel import Compel
+    COMPEL_AVAILABLE = True
+    print("✅ Compel available for long prompt processing")
+except ImportError:
+    COMPEL_AVAILABLE = False
+    print("⚠️ Compel not available - using standard prompt processing")
+
+# ===== 简化后的配置 =====
+STYLE_PRESETS = {
+    "None": "",
+    "Realistic": "photorealistic, 8k, ultra-detailed, cinematic lighting, masterpiece, realistic skin texture, detailed anatomy",
+    "Anime": "anime style, detailed, high quality, masterpiece, best quality, detailed eyes, perfect anatomy",
+    "Comic": "comic book style, bold outlines, vibrant colors, cel shading, dynamic pose",
+    "Watercolor": "watercolor illustration, soft gradients, pastel palette, artistic brush strokes"
+}
+
+# 固定模型配置
+FIXED_MODEL = "black-forest-labs/FLUX.1-dev"
+
+# 固定LoRA配置 - FLUX 专用 NSFW LoRA（假设从 CivitAI 下载）
+QUALITY_LORA = {
+    "repo_id": "path/to/flux-nsfw-lora",  # 替换为实际 NSFW LoRA（如 CivitAI 下载）
+    "filename": "flux-nsfw.safetensors",
+    "scale": 0.8,
+    "description": "NSFW and realism enhancer for FLUX"
+}
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+# 质量增强提示词（适配 NSFW）
+QUALITY_ENHANCERS = [
+    "detailed anatomy", "(perfect anatomy:1.2)", "soft skin", "natural lighting",
+    "high resolution", "(masterpiece:1.3)", "(best quality:1.2)",
+    "professional photography", "artistic composition",
+    "(perfect proportions:1.1)", "smooth textures", "intimate lighting",
+    "realistic skin texture", "(detailed face:1.1)", "natural pose"
 ]
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+# 风格专用增强词
+STYLE_ENHANCERS = {
+    "Realistic": ["photorealistic", "(ultra realistic:1.2)", "natural lighting", "detailed skin", "professional photography"],
+    "Anime": ["anime style", "(high quality anime:1.2)", "detailed eyes", "perfect face", "clean art style"],
+    "Comic": ["comic book style", "bold outlines", "vibrant colors", "cel shading"],
+    "Watercolor": ["watercolor style", "artistic", "soft gradients", "pastel palette"]
 }
-"""
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+SAVE_DIR = "generated_images"
+os.makedirs(SAVE_DIR, exist_ok=True)
 
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+# ===== 模型相关变量 =====
+pipeline = None
+compel_processor = None
+device = None
+model_loaded = False
+lora_loaded = False
+
+def initialize_model():
+    """优化的模型初始化函数"""
+    global pipeline, compel_processor, device, model_loaded, lora_loaded
+    
+    if model_loaded and pipeline is not None:
+        return True
+    
+    try:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"🖥️ Using device: {device}")
+        
+        print(f"📦 Loading fixed model: {FIXED_MODEL}")
+        
+        # 基础模型加载
+        pipeline = FluxPipeline.from_pretrained(
+            FIXED_MODEL,
+            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+            use_safetensors=True
+        )
+        
+        # 优化调度器
+        pipeline.scheduler = FlowMatchEulerDiscreteScheduler.from_config(
+            pipeline.scheduler.config
+        )
+        pipeline = pipeline.to(device)
+        
+        # 统一数据类型
+        if torch.cuda.is_available():
+            pipeline.transformer.to(torch.bfloat16)
+            pipeline.vae.to(torch.bfloat16)
+        
+        # GPU优化
+        if torch.cuda.is_available():
+            try:
+                pipeline.enable_vae_slicing()
+                pipeline.enable_model_cpu_offload()  # FLUX 高 VRAM 优化
+                pipeline.transformer = torch.compile(pipeline.transformer, mode="reduce-overhead")
+                print("✅ Torch.compile enabled for transformer")
+            except Exception as mem_error:
+                print(f"⚠️ Memory optimization warning: {mem_error}")
+        
+        # 加载 NSFW LoRA
+        try:
+            print("🔧 Loading NSFW LoRA...")
+            pipeline.load_lora_weights(
+                QUALITY_LORA["repo_id"],
+                weight_name=QUALITY_LORA["filename"],
+                adapter_name="nsfw_enhancer"
             )
+            pipeline.set_adapters(["nsfw_enhancer"], adapter_weights=[QUALITY_LORA["scale"]])
+            lora_loaded = True
+            print("✅ NSFW LoRA loaded successfully")
+        except Exception as lora_error:
+            print(f"⚠️ LoRA loading failed: {lora_error}")
+            lora_loaded = False
+        
+        # 初始化 Compel（FLUX 简化版）
+        if COMPEL_AVAILABLE:
+            try:
+                compel_processor = Compel(
+                    tokenizer=pipeline.tokenizer,
+                    text_encoder=pipeline.text_encoder,
+                    truncate_long_prompts=False
+                )
+                print("✅ Long prompt processor (Compel) initialized successfully")
+            except Exception as compel_error:
+                print(f"⚠️ Compel initialization failed: {compel_error}")
+                compel_processor = None
+        
+        model_loaded = True
+        print("✅ Model initialization complete")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Critical model loading error: {e}")
+        print(traceback.format_exc())
+        model_loaded = False
+        return False
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+def enhance_prompt(prompt: str, style: str) -> str:
+    """增强提示词"""
+    quality_terms = ", ".join(QUALITY_ENHANCERS)
+    
+    style_terms = ""
+    if style in STYLE_ENHANCERS:
+        style_terms = ", " + ", ".join(STYLE_ENHANCERS[style])
+    
+    style_suffix = STYLE_PRESETS.get(style, "")
+    
+    enhanced_parts = [prompt.strip()]
+    
+    if style_suffix:
+        enhanced_parts.append(style_suffix)
+    
+    if style_terms:
+        enhanced_parts.append(style_terms.lstrip(", "))
+    
+    enhanced_parts.append(quality_terms)
+    
+    enhanced_prompt = ", ".join(filter(None, enhanced_parts))
+    return enhanced_prompt
 
-        result = gr.Image(label="Result", show_label=False)
+def process_long_prompt(prompt, negative_prompt=""):
+    """处理长提示词（FLUX 简化版）"""
+    if not compel_processor:
+        return None
+    
+    try:
+        conditioning = compel_processor(prompt)
+        return conditioning
+    except Exception as e:
+        print(f"Long prompt processing failed: {e}")
+        return None
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+def apply_spaces_decorator(func):
+    """应用 spaces 装饰器"""
+    if SPACES_AVAILABLE:
+        return spaces.GPU(duration=60)(func)
+    return func
+
+def create_metadata_content(prompt, enhanced_prompt, seed, steps, cfg_scale, width, height, style):
+    """创建元数据内容"""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    return f"""Generated Image Metadata
+======================
+Timestamp: {timestamp}
+Original Prompt: {prompt}
+Enhanced Prompt: {enhanced_prompt}
+Seed: {seed}
+Steps: {steps}
+CFG Scale: {cfg_scale}
+Dimensions: {width}x{height}
+Style: {style}
+Model: FLUX.1-dev
+LoRA: NSFW Enhancer (scale: 0.8)
+"""
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+@apply_spaces_decorator
+def generate_image(prompt: str, style: str, negative_prompt: str = "", steps: int = 30, cfg_scale: float = 7.0, 
+                  seed: int = -1, width: int = 1024, height: int = 1024, progress=gr.Progress()):
+    """图像生成函数"""
+    if not prompt or prompt.strip() == "":
+        return None, "", ""
+    
+    # 初始化模型
+    progress(0.1, desc="Loading model...")
+    if not initialize_model():
+        return None, "", "❌ Failed to load model"
+    
+    progress(0.3, desc="Processing prompt...")
+    
+    try:
+        # 处理 seed
+        if seed == -1:
+            seed = random.randint(0, np.iinfo(np.int32).max)
+        
+        # 增强提示词
+        enhanced_prompt = enhance_prompt(prompt.strip(), style)
+        
+        # 增强负面提示词
+        if not negative_prompt.strip():
+            negative_prompt = "(low quality, worst quality:1.4), (bad anatomy, bad hands:1.2), blurry, watermark, signature, text, error, missing limbs, extra limbs, cropped, normal quality, jpeg artifacts, deformed, mutated"
+        
+        # 生成参数
+        generator = torch.Generator(device).manual_seed(seed)
+        
+        progress(0.5, desc="Generating image...")
+        
+        # 长提示词处理
+        use_long_prompt = len(enhanced_prompt.split()) > 60 or len(enhanced_prompt) > 300
+        
+        if use_long_prompt and compel_processor:
+            conditioning = process_long_prompt(enhanced_prompt)
+            
+            if conditioning is not None:
+                result = pipeline(
+                    prompt_embeds=conditioning,
+                    num_inference_steps=steps,
+                    guidance_scale=cfg_scale,
+                    width=width,
+                    height=height,
+                    generator=generator
+                )
+                image = result.images[0]
+            else:
+                result = pipeline(
+                    prompt=enhanced_prompt,
+                    negative_prompt=negative_prompt,
+                    num_inference_steps=steps,
+                    guidance_scale=cfg_scale,
+                    width=width,
+                    height=height,
+                    generator=generator
+                )
+                image = result.images[0]
+        else:
+            result = pipeline(
+                prompt=enhanced_prompt,
+                negative_prompt=negative_prompt,
+                num_inference_steps=steps,
+                guidance_scale=cfg_scale,
+                width=width,
+                height=height,
+                generator=generator
             )
+            image = result.images[0]
+        
+        progress(0.9, desc="Saving image...")
+        
+        # 保存图像
+        filename = f"IMG_{seed}.png"
+        filepath = os.path.join(SAVE_DIR, filename)
+        image.save(filepath, quality=95, optimize=True)
+        
+        # 创建元数据内容
+        metadata_content = create_metadata_content(
+            prompt, enhanced_prompt, seed, steps, cfg_scale, width, height, style
+        )
+        
+        progress(1.0, desc="Complete!")
+        
+        # 生成信息显示
+        generation_info = f"Prompt: {prompt}\nSeed: {seed} | Size: {width}×{height} | Steps: {steps} | CFG: {cfg_scale}"
+        
+        return image, generation_info, metadata_content
+        
+    except Exception as e:
+        error_msg = str(e)
+        print(f"Generation error: {error_msg}")
+        print(traceback.format_exc())
+        return None, "", f"❌ Generation failed: {error_msg}"
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+# ===== CSS 样式（保持不变） =====
+css = """
+/* 全局容器 */
+.gradio-container {
+    max-width: 100% !important;
+    margin: 0 !important;
+    padding: 0 !important;
+    background: linear-gradient(135deg, #e6a4f2 0%, #1197e4 100%) !important;
+    min-height: 100vh !important;
+    font-family: 'Segoe UI', Arial, sans-serif !important;
+}
 
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+/* 主要内容区域 */
+.main-content {
+    background: rgba(255, 255, 255, 0.95) !important;
+    border-radius: 20px !important;
+    padding: 20px !important;
+    margin: 15px !important;
+    box-shadow: 0 10px 25px rgba(0,0,0,0.2) !important;
+    min-height: calc(100vh - 30px) !important;
+    color: #3e3e3e !important;
+    backdrop-filter: blur(10px) !important;
+}
 
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+/* 简化标题 */
+.title {
+    text-align: center !important;
+    background: linear-gradient(45deg, #bb6ded, #08676b) !important;
+    -webkit-background-clip: text !important;
+    -webkit-text-fill-color: transparent !important;
+    background-clip: text !important;
+    font-size: 2rem !important;
+    margin-bottom: 15px !important;
+    font-weight: bold !important;
+}
 
+/* 简化警告信息 */
+.warning-box {
+    background: linear-gradient(45deg, #bb6ded, #08676b) !important;
+    color: white !important;
+    padding: 8px !important;
+    border-radius: 8px !important;
+    margin-bottom: 15px !important;
+    text-align: center !important;
+    font-weight: bold !important;
+    font-size: 14px !important;
+}
+
+/* 输入框样式 */
+.prompt-box textarea, .prompt-box input {
+    border-radius: 10px !important;
+    border: 2px solid #bb6ded !important;
+    padding: 15px !important;
+    font-size: 14px !important;
+    background: linear-gradient(135deg, rgba(245, 243, 255, 0.9), rgba(237, 233, 254, 0.9)) !important;
+    color: #2d2d2d !important;
+}
+
+.prompt-box textarea:focus, .prompt-box input:focus {
+    border-color: #08676b !important;
+    box-shadow: 0 0 15px rgba(77, 8, 161, 0.3) !important;
+    background: linear-gradient(135deg, rgba(255, 255, 255, 0.95), rgba(248, 249, 250, 0.95)) !important;
+}
+
+/* 右侧控制区域 */
+.controls-section {
+    background: linear-gradient(135deg, rgba(224, 218, 255, 0.8), rgba(196, 181, 253, 0.8)) !important;
+    border-radius: 12px !important;
+    padding: 15px !important;
+    margin-bottom: 10px !important;
+    border: 2px solid rgba(187, 109, 237, 0.3) !important;
+    backdrop-filter: blur(5px) !important;
+}
+
+.controls-section label {
+    font-weight: 600 !important;
+    color: #2d2d2d !important;
+    margin-bottom: 8px !important;
+}
+
+.controls-section input[type="radio"] {
+    accent-color: #bb6ded !important;
+}
+
+.controls-section input[type="number"], 
+.controls-section input[type="range"] {
+    background: rgba(255, 255, 255, 0.9) !important;
+    border: 1px solid #bb6ded !important;
+    border-radius: 6px !important;
+    padding: 8px !important;
+    color: #2d2d2d !important;
+}
+
+.controls-section select {
+    background: rgba(255, 255, 255, 0.9) !important;
+    border: 1px solid #bb6ded !important;
+    border-radius: 6px !important;
+    padding: 8px !important;
+    color: #2d2d2d !important;
+}
+
+/* 生成按钮 */
+.generate-btn {
+    background: linear-gradient(45deg, #bb6ded, #08676b) !important;
+    color: white !important;
+    border: none !important;
+    padding: 15px 25px !important;
+    border-radius: 25px !important;
+    font-size: 16px !important;
+    font-weight: bold !important;
+    width: 100% !important;
+    cursor: pointer !important;
+    transition: all 0.3s ease !important;
+    text-transform: uppercase !important;
+    letter-spacing: 1px !important;
+}
+
+.generate-btn:hover {
+    transform: translateY(-2px) !important;
+    box-shadow: 0 8px 25px rgba(187, 109, 237, 0.5) !important;
+}
+
+/* 图片输出区域 */
+.image-output {
+    border-radius: 15px !important;
+    overflow: hidden !important;
+    max-width: 100% !important;
+    max-height: 70vh !important;
+    border: 3px solid #08676b !important;
+    box-shadow: 0 8px 20px rgba(0,0,0,0.15) !important;
+    background: linear-gradient(135deg, rgba(255, 255, 255, 0.9), rgba(248, 249, 250, 0.9)) !important;
+}
+
+/* 图片信息区域 */
+.image-info {
+    background: linear-gradient(135deg, rgba(248, 249, 250, 0.9), rgba(233, 236, 239, 0.9)) !important;
+    border-radius: 8px !important;
+    padding: 12px !important;
+    margin-top: 10px !important;
+    font-size: 12px !important;
+    color: #495057 !important;
+    border: 2px solid rgba(187, 109, 237, 0.2) !important;
+    backdrop-filter: blur(5px) !important;
+}
+
+/* 保存按钮 */
+.save-btn {
+    background: linear-gradient(45deg, #28a745, #20c997) !important;
+    color: white !important;
+    border: none !important;
+    padding: 10px 20px !important;
+    border-radius: 8px !important;
+    font-size: 13px !important;
+    font-weight: 600 !important;
+    cursor: pointer !important;
+    margin-top: 8px !important;
+    margin-right: 10px !important;
+    transition: all 0.3s ease !important;
+}
+
+.save-btn:hover {
+    background: linear-gradient(45deg, #218838, #1ea471) !important;
+    transform: translateY(-1px) !important;
+    box-shadow: 0 4px 12px rgba(40, 167, 69, 0.3) !important;
+}
+
+/* 滑块样式 */
+.slider-container input[type="range"] {
+    accent-color: #bb6ded !important;
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+    .main-content {
+        margin: 10px !important;
+        padding: 15px !important;
+    }
+    
+    .title {
+        font-size: 1.5rem !important;
+    }
+}
+
+/* 隐藏占位符 */
+.gr-image .image-container:empty::before {
+    content: "Generated image will appear here" !important;
+    display: flex !important;
+    align-items: center !important;
+    justify-content: center !important;
+    height: 300px !important;
+    background: linear-gradient(135deg, rgba(248, 249, 250, 0.8), rgba(233, 236, 239, 0.8)) !important;
+    border-radius: 10px !important;
+    color: #6c757d !important;
+    font-size: 16px !important;
+    font-weight: 500 !important;
+    backdrop-filter: blur(5px) !important;
+}
+"""
+
+# ===== 创建 UI =====
+def create_interface():
+    with gr.Blocks(css=css, title="NSFW FLUX Image Generator") as interface:
+        with gr.Column(elem_classes=["main-content"]):
+            # 简化标题
+            gr.HTML('<div class="title">NSFW FLUX Image Generator</div>')
+            
+            # 简化警告信息
+            gr.HTML('''
+                <div class="warning-box">
+                    ⚠️ 18+ CONTENT WARNING ⚠️
+                </div>
+            ''')
+            
+            # 主要输入区域
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                # 左侧：提示词输入
+                with gr.Column(scale=2):
+                    prompt_input = gr.Textbox(
+                        label="Detailed Prompt",
+                        placeholder="Enter your detailed prompt here...",
+                        lines=12,
+                        elem_classes=["prompt-box"]
+                    )
+                    
+                    negative_prompt_input = gr.Textbox(
+                        label="Negative Prompt (Optional)",
+                        placeholder="Things you don't want in the image...",
+                        lines=4,
+                        elem_classes=["prompt-box"]
+                    )
+                
+                # 右侧：控制选项
+                with gr.Column(scale=1):
+                    # Style 选项
+                    with gr.Group(elem_classes=["controls-section"]):
+                        style_input = gr.Radio(
+                            label="Style Preset",
+                            choices=list(STYLE_PRESETS.keys()),
+                            value="Realistic"
+                        )
+                    
+                    # Seed 选项
+                    with gr.Group(elem_classes=["controls-section"]):
+                        seed_input = gr.Number(
+                            label="Seed (-1 for random)",
+                            value=-1,
+                            precision=0
+                        )
+                    
+                    # 宽度选择
+                    with gr.Group(elem_classes=["controls-section"]):
+                        width_input = gr.Slider(
+                            label="Width",
+                            minimum=512,
+                            maximum=2048,
+                            value=1024,
+                            step=64
+                        )
+                    
+                    # 高度选择
+                    with gr.Group(elem_classes=["controls-section"]):
+                        height_input = gr.Slider(
+                            label="Height",
+                            minimum=512,
+                            maximum=2048,
+                            value=1024,
+                            step=64
+                        )
+                    
+                    # 高级参数
+                    with gr.Group(elem_classes=["controls-section"]):
+                        steps_input = gr.Slider(
+                            label="Steps",
+                            minimum=10,
+                            maximum=50,
+                            value=30,
+                            step=1
+                        )
+                        
+                        cfg_input = gr.Slider(
+                            label="CFG Scale",
+                            minimum=1.0,
+                            maximum=15.0,
+                            value=7.0,
+                            step=0.1
+                        )
+                    
+                    # 生成按钮
+                    generate_button = gr.Button(
+                        "GENERATE",
+                        elem_classes=["generate-btn"],
+                        variant="primary"
+                    )
+            
+            # 图片输出区域
+            image_output = gr.Image(
+                label="Generated Image",
+                elem_classes=["image-output"],
+                show_label=False,
+                container=True
+            )
+            
+            # 图片信息和保存按钮
+            generation_info = gr.Textbox(
+                label="Generation Info",
+                interactive=False,
+                elem_classes=["image-info"],
+                show_label=False,
+                visible=False
+            )
+            
+            # 隐藏的变量存储
+            metadata_content = gr.Textbox(visible=False)
+            current_seed = gr.Number(visible=False)
+            current_image = gr.Image(visible=False)
+            
+            # 下载按钮区域
+            with gr.Row(visible=False) as download_row:
+                download_image_btn = gr.Button(
+                    "Save Image",
+                    elem_classes=["save-btn"],
+                    size="sm"
                 )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
+                
+                download_metadata_btn = gr.Button(
+                    "Save Metadata",
+                    elem_classes=["save-btn"],
+                    size="sm"
                 )
+        
+        # 生成图片的主要函数
+        def on_generate(prompt, style, neg_prompt, steps, cfg, seed, width, height):
+            image, info, metadata = generate_image(
+                prompt, style, neg_prompt, steps, cfg, seed, width, height
+            )
+            
+            if image is not None:
+                # 提取实际使用的 seed
+                actual_seed = seed if seed != -1 else int(info.split("Seed:")[1].split("|")[0].strip())
+                
+                return (
+                    image,  # 图片输出
+                    info,   # 生成信息
+                    metadata,  # 元数据
+                    actual_seed,  # 当前 seed
+                    image,  # 当前图片副本
+                    gr.update(visible=True),  # 显示生成信息
+                    gr.update(visible=True)   # 显示下载按钮区域
+                )
+            else:
+                return (
+                    None, 
+                    info, 
+                    "", 
+                    0,
+                    None,
+                    gr.update(visible=False),
+                    gr.update(visible=False)
+                )
+        
+        # 创建下载文件的函数
+        def create_download_image(image_data, seed_val):
+            if image_data is not None:
+                filename = f"IMG_{seed_val}.png"
+                filepath = os.path.join(SAVE_DIR, filename)
+                image_data.save(filepath, quality=95, optimize=True)
+                return filepath
+            return None
+            
+        def create_download_metadata(metadata_text, seed_val):
+            if metadata_text:
+                filename = f"IMG_{seed_val}.txt"
+                filepath = os.path.join(SAVE_DIR, filename)
+                with open(filepath, 'w', encoding='utf-8') as f:
+                    f.write(metadata_text)
+                return filepath
+            return None
+        
+        # 绑定生成事件
+        generate_button.click(
+            fn=on_generate,
+            inputs=[
+                prompt_input, style_input, negative_prompt_input, 
+                steps_input, cfg_input, seed_input, width_input, height_input
+            ],
+            outputs=[
+                image_output, generation_info, metadata_content, 
+                current_seed, current_image, generation_info, download_row
+            ],
+            show_progress=True
+        )
+        
+        # 支持 Enter 键触发
+        prompt_input.submit(
+            fn=on_generate,
+            inputs=[
+                prompt_input, style_input, negative_prompt_input, 
+                steps_input, cfg_input, seed_input, width_input, height_input
+            ],
+            outputs=[
+                image_output, generation_info, metadata_content, 
+                current_seed, current_image, generation_info, download_row
+            ],
+            show_progress=True
+        )
+        
+        # 下载图片
+        def handle_image_download(image_data, seed_val):
+            filepath = create_download_image(image_data, seed_val)
+            if filepath:
+                return gr.File(value=filepath, visible=True)
+            return gr.File(visible=False)
+        
+        download_image_btn.click(
+            fn=handle_image_download,
+            inputs=[current_image, current_seed],
+            outputs=[gr.File()]
+        )
+        
+        # 下载元数据
+        def handle_metadata_download(metadata_text, seed_val):
+            filepath = create_download_metadata(metadata_text, seed_val)
+            if filepath:
+                return gr.File(value=filepath, visible=True)
+            return gr.File(visible=False)
+        
+        download_metadata_btn.click(
+            fn=handle_metadata_download,
+            inputs=[metadata_content, current_seed],
+            outputs=[gr.File()]
+        )
+        
+        # 启动时显示欢迎信息
+        interface.load(
+            fn=lambda: (
+                None, "", "", 0, None,
+                gr.update(visible=False), 
+                gr.update(visible=False)
+            ),
+            outputs=[
+                image_output, generation_info, metadata_content, 
+                current_seed, current_image, generation_info, download_row
+            ]
+        )
+    
+    return interface
 
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
+# ===== 启动应用 =====
 if __name__ == "__main__":
-    demo.launch()
+    print("🎨 Starting NSFW FLUX Image Generator...")
+    print(f"🔧 Fixed Model: {FIXED_MODEL}")
+    print(f"🔧 NSFW LoRA: {QUALITY_LORA['description']}")
+    print(f"🔧 Spaces GPU: {'✅ Available' if SPACES_AVAILABLE else '❌ Not Available'}")
+    print(f"🔧 Compel Library: {'✅ Available' if COMPEL_AVAILABLE else '❌ Not Available'}")
+    print(f"🔧 CUDA: {'✅ Available' if torch.cuda.is_available() else '❌ Not Available'}")
+    
+    app = create_interface()
+    app.queue(max_size=10, default_concurrency_limit=2)
+    
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        share=False
+    )